The European Physical Journal Special Topics

, Volume 216, Issue 1, pp 73–81 | Cite as

Maximal entropy random walk in community detection

  • J.K. OchabEmail author
  • Z. BurdaEmail author
Regular Article


The aim of this paper is to check feasibility of using the maximal-entropy random walk in algorithms finding communities in complex networks. A number of such algorithms exploit an ordinary or a biased random walk for this purpose. Their key part is a (dis)similarity matrix, according to which nodes are grouped. This study en- compasses the use of a stochastic matrix of a random walk, its mean first-passage time matrix, and a matrix of weighted paths count. We briefly indicate the connection between those quantities and propose substituting the maximal-entropy random walk for the previously chosen models. This unique random walk maximises the entropy of ensembles of paths of given length and endpoints, which results in equiprobability of those paths. We compare the performance of the selected algorithms on LFR benchmark graphs. The results show that the change in performance depends very strongly on the particular algorithm, and can lead to slight improvements as well as to significant deterioration.


Random Walk European Physical Journal Special Topic Community Detection Stochastic Matrix Cayley Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© EDP Sciences and Springer 2013

Authors and Affiliations

  1. 1.Marian Smoluchowski Institute of Physics and Mark Kac Complex Systems Research Center Jagiellonian UniversityKrakówPoland

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